Ontology types

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Ontologies provide a structured framework for organizing and representing knowledge about the physical system being modelled. In the context of digital twins, ontologies enable the representation of entities, their attributes, relationships, and behaviours, allowing for a comprehensive understanding and simulation of the system. By defining a shared vocabulary and standardized semantics, ontologies facilitate interoperability and data exchange between different components of the digital twin ecosystem, such as sensors, models, and analytics systems. They enable seamless integration of heterogeneous data sources and support advanced analytics, reasoning, and decision-making capabilities. Additionally, ontologies help in capturing domain-specific knowledge, supporting context-awareness, and enabling intelligent interactions with the digital twin, ultimately enhancing its accuracy, reliability, and usability in various applications across industries.

Ontology types

To enable effective communication and interoperability between different digital twin models and applications, ontologies play a crucial role. Ontologies provide a standardized vocabulary and set of relationships that define the concepts, properties, and interactions within a specific domain. Here are some of the main ontologies commonly used in digital twins:

Semantic Sensor Network (SSN) Ontology

Web page:  https://www.w3.org/TR/2017/REC-vocab-ssn-20171019/

The SSN ontology is designed to represent and reason about sensor data and observations. It provides a formal representation for describing sensors, observations, and their properties, allowing digital twins to integrate and interpret real-time sensor information.


Asset Administration Shell (AAS) Ontology

Web page:  https://industrialdigitaltwin.org/wp-content/uploads/2022/06/DetailsOfTheAssetAdministrationShell_Part1_V3.0RC02_Final1.pdf

AAS is an ontology for describing the digital representation of physical assets in the Industrial Internet of Things (IIoT) context. It provides a standardized model for representing the structure, behaviour, and relationships of assets within a digital twin, enabling seamless communication between different systems.


Product Lifecycle Management (PLM) Ontology

Web page:  https://www.wrike.com/product-management-guide/faq/what-is-product-lifecycle-management/

The PLM ontology is used to represent the lifecycle stages of a product, including design, manufacturing, maintenance, and disposal. It facilitates the integration of product-related information throughout its entire lifecycle, enabling digital twins to capture and simulate the product's behaviour and performance at different stages.


Smart City Ontology

Web page:  https://github.com/Azure/opendigitaltwins-smartcities/

Smart city ontologies focus on representing the various aspects of urban environments and infrastructure. They capture information about buildings, transportation systems, energy networks, and other city components, enabling digital twins to simulate and optimize urban operations and services.


Building Information Modelling (BIM) Ontology

Web page:  https://www.bimframework.info/

BIM ontologies are specific to the construction and building industry. They provide a standardized representation of building components, their relationships, and attributes. BIM ontologies enhance digital twins by incorporating architectural, structural, and mechanical information, facilitating better visualization, analysis, and decision-making.


Manufacturing Ontology

Web page:  https://github.com/MuhammadYahta/rgom

Manufacturing ontologies capture the knowledge, processes, and relationships involved in manufacturing operations. They encompass manufacturing equipment, processes, materials, and quality control aspects, allowing digital twins to simulate and optimize production processes.


Azure Digital Twins

Web page:  https://azure.microsoft.com/en-us/products/digital-twins

Open modelling language to create custom domain models of any connected environment using Digital Twins Definition Language.


These ontologies, along with others specific to different domains, contribute to the development of comprehensive digital twin ecosystems. They provide a shared understanding of the represented entities, their attributes, and their relationships, enabling effective collaboration and integration among different digital twin systems and applications.

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